An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Adaptive learning and mining for data streams and frequent patterns
ACM SIGKDD Explorations Newsletter
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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Several classical schemes exist to represent trees as strings over a fixed alphabet; these are useful in many algorithmic and conceptual studies. Our previous work has proposed a representation of unranked trees as strings over a countable alphabet, and has shown how this representation is useful for canonizing unordered trees and for mining closed frequent trees, whether ordered or unordered. Here we propose a similar, simpler alternative and adapt some basic algorithmics to it; then we show empirical evidence of the usefulness of this representation for mining frequent closed unordered trees on real-life data.